We use the SimCLR setup [1]} to study unsupervised disentanglement: encoder outputs \(r_i\)  are fed into a projection network and the contrastive loss is computed on projection outputs \(z_i\)  for \(i\)  in a batch of size \(N\) .
We evaluate disentanglement
on subembeddings \(r_{i,k}\)  or \(z_{i,k}\) , where \(k\in [K]\)  is a slice of \(r_i\)  or \(z_i\) .
The InfoNCE loss [2]}, which we refer to as InfoMax, is:
\(\mathcal {L_{\text{InfoMax}}} = -\sum _{i \in I} \log {\frac{\exp ({z_i \cdot z_{j} / \tau })}{\sum \limits _{a\in A}\exp ({z_i \cdot z_{a} / \tau })}}\) 
